Network Integrated Diagnostic System and Predictive Analysis Tool for Mitigating Service Impacts on Application Services

ABSTRACT

A diagnostic system for determining and predicting service impacts to network connected applications. The diagnostic system stores static variables for each application in the network and receives real-time data for applications in the network. Each static variable comprises a processing time frame that is predetermined and considered an acceptable time for executing an application and a time threshold that is predetermined and considered an acceptable time for executing another application functionally dependent thereon. The real-time data comprise a time delay value for each application and is a measured time for executing an application. The system compares a time delay with a processing time frame for an application. The comparison result is used to determine a service impact for the application. The system predicts a probability of another service impact for a functionally dependent application using an algorithmic model, the service impact, and a time threshold for the dependent application.

TECHNICAL FIELD

The present disclosure relates generally to computer networks andapplication services, and more specifically to a network integrateddiagnostic system and predictive analysis tool for mitigating serviceimpacts on application services.

BACKGROUND

Application services can be used in many industries to perform varioustasks. These tasks can relate to something as simple as triggering anemail item in response to a change in folder status, controllingoperation of an automobile in response to a multitude of sensorreadings, and performing a batch transactional operation on timesensitive data. In some instances, such as triggering an email item inresponse to a change in folder status, time may not be critical but inthe latter two instances timing as it relates to processing andcommunication of application workflow data can be critical. Disruptionsin the processing and communication of application workflow data canimpact an application service being rendered and, therefore, result inthe loss and/or misuse of network bandwidth and processing resources.

Depending on the application service, a service rendered can bedependent on hundreds of supportive application and networking services.In some cases, some of the supportive applications and networkingservices may not be under a single service provider’s control. As anexample, application and networking services required to perform batchtransactional operations on time sensitive workflow data may bestructured in such a way that an originating entity may perform certainapplication and networking functions associated with an applicationservice and one or more vendors may perform other application andnetworking functions associated with the application service.Maintenance delays are inherent in any type of application service thatexperiences a service disruption. These maintenance delays can bemagnified when the service being rendered requires the assistance ofhundreds of applications in rendering the service and is a multipartyservice, or both. Regardless of how a network is structured or thenumber of applications used to render a particular service, efficientprocessing and communication of application workflow data are importantto maintain a satisfactory level of service and satisfy any establishedservice level requirements.

SUMMARY

The present disclosure describes a network integrated diagnostic systemand predictive analysis tool used to identify disruptions in applicationworkflow data of an application service, determine and predict an impactto one or more applications, and render an analysis report toresponsible parties. The network integrated diagnostic system andpredictive analysis tool are integrated with multiple system processors,multiple communication protocol stacks, and a plurality of applicationservices and used to detect disruptions in application workflow data.The network integrated diagnostic system and predictive analysis toolimproves state of the art application services and network technology bydetecting inefficiencies in the processing and communication ofreal-time application data associated with an application service orservices.

In an example use case, a primary entity and one or more secondaryentities are responsible for rendering a time-sensitive applicationservice comprising a set of applications (application workflow) thatfunction together to perform a specific task. The application servicecomprises hundreds of applications that provide application and networkfunctions for enabling the application service. The primary entity andthe one or more secondary entities are bound by one or more servicelevel agreements.

In a practical application of the network integrated diagnostic systemand predictive analysis tool, the network integrated diagnostic systemand predictive analysis tool monitors real-time workflow data generatedby the applications, detects disruptions of real-time workflow data,e.g. based on a data rate or an aggregated data rate, and determines animpact to an application function and potential impacts to dependentapplication functions. The network integrated diagnostic system andpredictive analysis tool then determines if the disruptions aresignificant enough to breach the one or more service level agreementsand, therefore, have a negative impact on the application service. Areport is generated identifying the application service, the impactedapplication functions, their dependencies, and the effects, or potentialthereof, the impacted functions have on the one or more service levelagreements. The report is then used to remedy the issue in a timelymanner so that network and processing resources are used in a moreefficient and timely manner. For example, the processing and memoryresources being reserved for idle applications may be redirected toperform other tasks. In addition, other sources of real-time workflowdata may be identified and used to replace the disrupted real-timeworkflow data so as to avoid wasting networking, processing, and memoryresources.

The network integrated diagnostic system and predictive analysis toolprovides an improvement over existing networking technology in that itcan detect disruptions in real-time workflow data in an applicationworkflow and the potential for disruptions to real-time workflow data inthe application workflow and whether the disruptions are significantenough to have a negative impact on the quality of an applicationservice based on a predetermined level of quality of service. Based onthis information, the operation of application services in the workflowthat are affected by the potential disruptions can be modified to avoidthe misuse and waste of networking and processing resources.

To that end, presented herein is a system that comprises a memory and aprocessor configured by instructions to identify disruptions inapplication workflow data of an application service, determine andpredict an impact to one or more applications, and render an analysisreport. The memory is configured to store static variables for aplurality of applications. The static variables comprise a processingtime frame to satisfy a service level requirement for a firstapplication and a time threshold to satisfy a service level requirementfor a second application. The processor is also configured to: receivereal-time data for a first application, wherein the first application isassociated with the second application in a workflow, the real-time datafor the first application comprises a time delay associated withexecuting the first application; compare the real-time data for thefirst application with the static variables for the first application;determine a first service impact for the first application based on thecomparison of the real-time data for the first application with thestatic variables for the first application; predict a second serviceimpact for the second application based on the first service impact forthe first application and the time threshold associated with the secondapplication; generate a service impact report according to thedetermined first service impact and the predicted second service impact;and send the service impact report to at least one recipient computingdevice.

In some embodiments, the processor is further configured to: receivereal-time data for the second application, wherein the secondapplication is associated with a third application in the workflow;wherein the static variables further comprise a second processing timeframe required to satisfy a service level requirement for the secondapplication and a second time threshold required to satisfy a servicelevel requirement for the third application; compare the real-time dataof the second application with the static variables of the secondapplication; determine a third service impact for the second applicationbased on the comparison of the real-time data for the second applicationwith the static variables for the second application; predict a fourthservice impact for the third application based on the third serviceimpact for the second application and the second time thresholdassociated with the third application; generate a second service impactreport according to the determined third service impact and thepredicted fourth service impact; and send the second service impactreport to the at least one recipient computing device.

In other embodiments, the applications are network connected andconfigured to perform a time sensitive network service using at leastone hand-shake signal between the first application and at least oneother application from the plurality of applications. In some of theseembodiments, the processor is further configured to determine the timedelay associated with executing the first application based at least inpart upon the at least one hand-shake signal. In still yet otherembodiments, the processor is further configured to compare the timedelay associated with executing the first application against athreshold value associated with the first application to determine thefirst service impact. In yet other embodiments, the processor is furtherconfigured to predict the service impact using a knowledge base and analgorithmic model. Yet in other embodiments, the processor is furtherconfigured to determine an effect of the service impact based the staticvariables.

Certain embodiments of this disclosure may include some, all, or none ofthese advantages. These advantages and other features will be moreclearly understood from the following detailed description taken inconjunction with the accompanying drawings and claims.

BRIEF DESCRIPTION OF THE DRAWINGS

For a more complete understanding of this disclosure, reference is nowmade to the following brief description, taken in connection with theaccompanying drawings and detailed description, wherein like referencenumerals represent like parts.

FIG. 1 illustrates an embodiment of a network integrated diagnosticsystem and predictive analysis tool; and

FIG. 2 illustrates an example operational flow of the network integrateddiagnostic system and predictive analysis tool of FIG. 1 .

DETAILED DESCRIPTION

As described above, previous technologies fail to identify disruptionsin application workflow data of an application service, determine andpredict an impact to one or more applications, and render an analysisreport. This disclosure addresses those limitations. FIG. 1 illustratesan example embodiment of an application service architecture 100 thatdetects disruptions in workflow data of an application workflow,determines and predicts a service impact to one or more applications ofthe application workflow, and renders an analysis report. FIG. 2 is aflow diagram depicting the operational flow 200 of the applicationservice architecture 100 of FIG. 1 , according to certain exampleembodiments.

Example System for Implementing the Network Integrated Diagnostic Systemand Predictive Analysis Tool

The application service architecture 100 comprises a plurality ofnetwork connected applications 102 and a network integrated diagnosticsystem and predictive analysis tool 105 communicatively coupled to theplurality of applications 102 through a network 106 and a networkinterface 108. The network integrated diagnostic system and predictiveanalysis tool 105 can also be communicatively coupled to one or moreuser computing devices 110. The network connected applications 102 aregrouped into application workflows 104 a-104 c. Each applicationworkflow 104 a-104 c is configured to render an application service.Workflow 104 a comprises applications 102 a-102 c, workflow 104 bcomprises applications 102 d-f, and workflow 104 c comprisesapplications 102 g-i. Each of the applications 102 a-102 c, applications102 d-102 f, and applications 102 g-102 i are configured to perform oneor more functions in support of rendering an application service. Theapplication workflows 104 a-104 c generate workflow data 112 a-112 b,112 c-112 d, and 112 e-112 f and real-time data 114 a-114 b, 114 c-114d, and 114 e-114 f. Application 102 b is functionally dependent onapplication 102 a and application 102 c is functionally dependent onapplication 102 b. A similar dependency applied to applications 102a-102 c also applies to applications 102 d-102 i. Moreover, workflowdata 112 b is dependent on workflow data 112 a, workflow data 112 d isdependent on workflow data 112 c, and workflow data 112 f is dependenton workflow data 112 e. Although FIG. 1 illustrates only threeapplication workflows 104 with each application workflow 104 having onlythree applications 102, it should be understood that in practice therecan be any suitable number and combination of application workflows 104and associated applications 102. The tool 105 comprises a processor 116and a memory 118 communicatively coupled to processor 116. Memory 118stores an instruction set 122, application static variables 124, andreal-time data 114 a-114 b, 114 c-114 d, and 114 e-114 f. Each of theapplications 102 a-102 i have static variables 124 a-124 i associatedtherewith. Memory 118 may be implemented using any suitable storagesystem, such as a database, and may store any suitable information anddata used in the operation of architecture 100.

In general, tool 105 is configured to identify disruptions in workflowdata of an application workflow 104 a, 104 b, or 104 c, determine andpredict a service impact to one or more of the applications 102 withinthe application workflows 104 a-104 c, and render a service impactreport 126. In certain embodiments, the service impact report 126 maythen be used to remedy the issue in a timely manner so that network,processing, and memory resources can be used in a more efficient andtimely manner. For example, the processing and memory resources beingreserved for idle applications 102 may be redirected to perform othertasks. In addition, other sources of workflow data 112 may be identifiedand used to replace the disrupted workflow data 112 to avoid wastingprocessing and memory resources. As previously stated, an applicationservice is a system function that is generated by applications 102 a-c,102 d-f, or 102 g-i of the application workflows 104 a-104 c workingtogether in a coordinated manner. A service impact is a delay inprocessing application workflow data 112 a-112 b, 112 c-112 d, or 112e-112 f in application workflows 104 a-104 c that is significant enoughto default on one or more service level agreements.

System Components Network

Network 106 may be any suitable type of wireless and/or wired network,including, but not limited to, all or a portion of the Internet, anIntranet, a private network, a public network, a peer-to-peer network,the public switched telephone network, a cellular network, a local areanetwork (LAN), a metropolitan area network (MAN), a wide area network(WAN), and a satellite network. The network 106 may be configured tosupport any suitable type of communication protocol as would beappreciated by one of ordinary skill in the art.

Network Integrated Diagnostic System and Predictive Analysis Tool

The network integrated diagnostic system and predictive analysis tool105 is a device that is configured to process data and communicate withthe plurality of applications 102 via the network 106. Tool 105 isgenerally configured to identify disruptions in workflow data of anapplication workflow function to render an application service,determine and predict an impact to one or more applications, and renderan analysis report to responsible parties. This operation of the networkintegrated diagnostic system and predictive analysis tool 105 isdescribed further below in conjunction with the operational flow 200 ofFIG. 2 .

The network integrated diagnostic system and predictive analysis tool105 comprises processor 116 in signal communication with networkinterface 108 and memory 118. Memory 118 stores a software instructionset 122 that when executed by the processor 116, cause the networkintegrated diagnostic system and predictive analysis tool 105 to performone or more functions described herein. For example, when the softwareinstruction set 122 is executed, tool 105 identifies disruptions inworkflow data 112 a-112 f of an application workflow 104 a, 104 b, or104 c to render an application service, determines and predicts animpact to one or more of the plurality of applications 102, and rendersan impact service report 126 to the user computing device 110, systemtools used in the maintenance of application functions, or both. Incertain embodiments, the service impact report 126 may then be used toremedy the issue in a timely manner so that network, processing, andmemory resources can be used in a more efficient and timely manner. Forexample, the processing and memory resources being reserved for idleapplications 102 may be redirected to perform other tasks. In addition,other sources of workflow data 112 may be identified and used to replacethe disrupted workflow data 112 to avoid wasting processing and memoryresources. The network integrated diagnostic system and predictiveanalysis tool 105 may be configured as shown, or in any otherconfiguration.

Processor 116 is any electronic circuitry, including, but not limitedto, state machines, one or more central processing unit (CPU) chips,logic units, cores (e.g., a multi-core processor), field-programmablegate arrays (FPGAs), application-specific integrated circuits (ASICs),or digital signal processors (DSPs). The processor 116 may be aprogrammable logic device, a microcontroller, a microprocessor, or anysuitable combination of the preceding. The processor 116 iscommunicatively coupled to and in signal communication with the networkinterface 108 and memory 118. The processor 116 is configured to processdata and may be implemented in hardware or software. For example,processor 116 may be 8-bit, 16-bit, 32-bit, 64-bit, or of any othersuitable architecture. The processor 116 may include an arithmetic logicunit (ALU) for performing arithmetic and logic operations, processorregisters that supply operands to the ALU and store the results of ALUoperations, and a control unit that fetches instructions from memory andexecutes them by directing the coordinated operations of the ALU,registers and other components. The processor 116 is configured toimplement various instructions. For example, the processor 116 isconfigured to execute the software instruction set 122 to implement thefunctions disclosed herein, such as some or all of those described withrespect to FIGS. 1-2 . In some embodiments, the function describedherein is implemented using logic units, FPGAs, ASICs, DSPs, or anyother suitable hardware or electronic circuitry.

Network interface 108 is configured to enable wired and/or wirelesscommunications (e.g., via the network 106). The network interface 108 isconfigured to communicate data between the network integrated diagnosticsystem and predictive analysis tool 105 and other network devices,systems, or domain(s). For example, the network interface 108 maycomprise a WIFI interface, a local area network (LAN) interface, a widearea network (WAN) interface, a modem, a switch, or a router. Theprocessor 116 is configured to send and receive data using the networkinterface 108. The network interface 108 may be configured to use anysuitable type of communication protocol.

Memory 118 may be volatile or non-volatile and may comprise a read-onlymemory (ROM), random-access memory (RAM), ternary content-addressablememory (TCAM), dynamic random-access memory (DRAM), and staticrandom-access memory (SRAM). Memory 118 may be implemented using one ormore disks, tape drives, solid-state drives, and/or the like. Memory 118is operable to store the software instruction set 122, static variables124, real-time data 114 a-114 f, and/or any other data or instructions.The software instruction set 122 may comprise any suitable set ofinstructions, logic, rules, or code operable to execute the processor116.

The operation of the disclosed architecture 100 is described inconjunction with the operational flow 200 described in FIG. 2 .

Operational Flow

In operation, the network integrated diagnostic system and predictiveanalysis tool 105 determine delays in processing and communicatingworkflow data 112 a-112 f, determines and predicts if the delays willcause a service impact to one or more applications 102 a-102 i of theapplication workflows 104 a-104 c, and renders a service impact report126 if a positive determination is realized. The service impact report126 is used by automated service maintenance tools or by other means toremedy the cause of the delays.

The plurality of network connected applications 102 can comprise one ormore applications belonging to a primary entity responsible forrendering application functions in support of application servicesand/or one or more applications belonging to third-party vendorsresponsible for rendering application functions in support ofapplication services. Network 106 can comprise the Internet and multipleintranets and can comprise network computing devices associated with aprimary entity responsible for rendering select application functionsfor application services and one or more third party vendors responsiblefor rendering select application functions for application services. Incertain embodiments, these types of third-party arrangements mayintroduce delays in service maintenance when a service impact isrealized. The network 106 is an abstract representation of networkedcomputing devices. The networked computing devices themselves are notillustrated in FIG. 1 but would be readily understood as component partsthereof by anyone skilled in the art.

As previously stated, each application 102 a-102 i in the applicationworkflows 104 a-104 c is configured to perform a particular applicationfunction based on an application service that is being rendered.Disruptions or interrupts in the execution of the applications 102 a-102i can result in delays in the processing and communication of theworkflow data 112 a-112 f. In an embodiment, the tool 105 can detect thedisruptions or interrupts by monitoring the real-time data 114 a-114 b,114 c-114 d, and 114 e-114 f and either derive measurements therefrom oruse measurements that have been derived therefrom.

The real-time data 114 a-114 b, 114 c-114 d, and 114 e-114 f are dataeither generated by a function of an application 102 a-102 i of theapplication workflows 104 a-104 c or a network application service ofthe networked computing devices from network 106. The real-time data 114a-114 b, 114 c-114 d, 114 e-114 f are used to determine if delays arepresent in the processing and communications of workflow data 112 a-112b, 112 c-112 d, 112 e-112 f. As an example type of real-time data 114a-114 b, 114 c-114 d, 114 e-114 f that can be used to determine delays,the applications 102 a-102 i, or networked computing devices associatedtherewith, of the application workflows 104 a-104 c generate real-timedata 114 a-114 b, 114 c-114 d, 114 e-114 f in response to processing andcommunicating the workflow data 112 a-112 f. The real-time data 114a-114 b, 114 c-114 d, 114 e-114 f may indicate a start of networkcommunications and an end of network communications. This type ofcommunication process is referred to as a handshake messaging, orsignaling, process. TCP is an example of a protocol that uses this typeof handshake messaging process. In this example, the real-time data 114a-114 f may comprise a data sequencing initiation message and a datacompletion acknowledgement message. The data sequencing initiationmessage and a data completion acknowledgement message can be used tomeasure a Round Trip Time (RTT) value. In some embodiments, the RTTvalues may be a system variable available for inspection. The RTTvariable value can be used to determine an amount of time to process andcommunicate workflow data 112 a-112 f for an application 102 a-102 i inan application workflow 104 a-104 c. Delta T₁ and delta T₂, in FIG. 1 ,for workflow data 112 a-112 f, respectively, are examples of real-timedata and are the measured processing and communication times(application execution times) of first application 102 a and secondapplication 102 b to process and communicate the workflow data 112 a-112f. Although TCP is specifically described herein, other protocols usedin the communication process may also have RTT variables available on asystem that are suitable for the intended purpose described above.

The applications 102 a-102 i of the application workflows 104 a-104 chave associated therewith static variables 124 a-124 i. The staticvariables 124 a-124 i are stored in memory 118, and are set based on aspecification for the applications 102 a-102 i in the workflows 104a-104 c and the service level agreements associated with theapplications 102 a-102 i. In one embodiment, static variables 124 a-124i include predefined processing time frame data, time threshold data,and service level requirements and terms associated therewith. Theprocessing time frame data are acceptable amounts of time, based onservice level agreements, for applications 102 a-102 i in theapplication workflows 104 a-104 c to process and communicate workflowdata 112 a-112 f. The time threshold data are acceptable amounts oftime, based on service level agreements, for applications and dependentapplications in the application workflows 104 a-104 c to process andcommunicate workflow data 112 a-112 f.

The instruction set 122 can be in distributed form wherein theinstruction set 122 is integrated with various applications within theapplication service architecture 100; in a centralized form wherein theapplication workflows 104 a-104 c of the plurality of applications 102and the networked computing devices of the network 106 feed real-timedata 114 a-114 f to a centralized processing system; or a combinationthereof. The various applications 102 can include an application 102that is part of an application workflow 104 a-104 c, an application 102that manages communications in a protocol stack, such as TCP, and anapplication 102 used to manage process logs for a particular networkedcomputing device or a cluster of networked computing devices.

The memory 118 can be cache memory and swap memory used to temporarilystore the instruction set 122, static variables 124, real-time data 114a-114 f, algorithmic predictive analysis models, and any other data usedin the execution of an application function. In practice, theinstruction set 122 is executed by the processor 116 to monitorapplication workflows 104 a-104 c, receive real-time data 114 a-114 fduring operation of application workflows 104 a-104 c, determine serviceimpacts 126 using the real-time data 114 a-114 f and static variables124, and, in real-time, generate reports when conditions indicate adisruption in an application function, application service, or both.

The network infrastructure arrangement of instruction set 122 and thetype of memory 118 used are arranged to minimize delays in determiningand predicting service impacts. In Big Data type applications, executingthe instruction set 122 or component parts thereof in cache memory maybe required in order to minimize computational delays that could resultin loss and/or misuse of the application service architecture 100.

In a practical application, the first application workflow 104 a ofapplication service architecture 100 can be used to render batchprocessing jobs (application service) with the first application 102 adedicated to performing a first type of application function, the secondapplication 102 b dedicated to performing a second type of applicationfunction, and the third application 102 c dedicated to performing athird type of application function. Each application 102 a, 102 b, and102 c is bound by a service level agreement. As an example, theagreement associated with the application service may require the firstapplication 102 a to perform the first type of application function onpredetermined amounts of data at specified times each day within aprocessing time frame for the first application 102 a; the secondapplication 102 b to perform the second type of application function onthe output from the first application 102 a within a processing timethreshold for second application 102 b; and the third application 102 cto perform the third type of application function on the output from thesecond application 102 b within a processing time threshold of the thirdapplication 102 c. The processing time threshold of the secondapplication 102 b is the sum of the processing time of the firstapplication 102 a plus an application execution time for the applicationfunction of the second application 102 b. The processing time thresholdof the third application 102 c is the sum of the processing time of thefirst application 102 a, the processing time of second application 102b, and the processing time of the third application 102 c.

The processor 116 can receive real-time data 114 a generated by thefirst application 102 a and the second application 102 b in theapplication workflow 104 a and determine a time delay (delta T₁ fromFIG. 1 ) associated with the real-time data 114 a. The time delay (deltaT₁) is the delay introduced into the application service as a result ofexecuting the first application 102 a. The processor 116 can thencompare the time delay (delta T₁) for the first application 102 a withstatic variables 124 for the first application 102 a. The processor 116can then determine a first service impact 128 a for the firstapplication 102 a if the results of the comparison exceeds theprocessing time frame for the first application 102 a. The processor 116can then use an algorithmic model, e.g. an a-priori knowledge base, anAI based algorithmic model, an ML based algorithmic model, or anycombination thereof, to predict a second service impact 128 b for thesecond application 102 b. For the second application 102 b, theprocessor 116 uses the first service impact 128 a for the firstapplication 102 a and the time threshold for the second application 102b as determined from the static variables 124 to determine a probabilityof whether service of the second application 120 is likely to beimpacted. For example, if the second application 102 b is expected toreceive workflow data 112 a from first application 102 a by 8:00 am on aparticular day, but the real-time data 114 a indicates that firstapplication 102 a is experiencing processing delays such that workflowdata 112 a won’t be available until 10:00 am on that particular day,then processor 116 can predict second service impact 128 b. Theprocessor 116 can then generate a first service impact report 126 aaccording to the determined first service impact 128 a and the predictedsecond service impact 128 b and send the service impact report 126 a toa recipient at the user computing device 110.

In certain embodiments, the service impact report 126 a may then be usedto remedy the issue in a timely manner so that network, processing, andmemory resources can be used in a more efficient and timely manner. Forexample, processing and memory resources being reserved for secondapplication 102 b may be redirected to perform other tasks during thetime of the delay in receiving workflow data 112 a from firstapplication 102 a. In addition, processor 116 may identify other sourcesof workflow data 112 a (e.g., applications 102 other than firstapplication 102 a) and route the workflow data 112 a from thatalternative source application 102 on a more timely basis so thatprocessing and memory resources used for second application 102 b arenot wasted.

In addition, the processor 116 can receive real-time data 114 bgenerated by the second application 102 a and the third application 102c in the application workflow 104 a and determine a time delay (delta T₂from FIG. 1 ). As previously mentioned, the time delay can be determinedbased on an RTT value. The static variables 124 comprise the processingtime threshold for the second application 102 b and the processing timethreshold for the third application 102 c. The processor 116 can comparethe time delay (delta T₂) with the processing time threshold of thesecond application 102 b. If the time delay exceeds the processing timethreshold, the processor 116 can generate a third service impact 128 cfor the second application 102 b based on the comparison. The processor116 can then use an algorithmic model, e.g. an a-priori knowledge base,an AIbased algorithmic model, an ML based algorithmic model, or anycombination thereof, to predict a fourth service impact 128 d for thethird application 102 c. For the third application 102 c, the processor116 uses the third service impact 128 c for the second application 102b, and the processing time threshold associated with the thirdapplication 102 c as determined from the static variables 124 todetermine a probability of whether service of the third application 102c is likely to be impacted. The processor 116 can then generate a secondservice impact report 126 b according to the determined third serviceimpact 128 c and the predicted fourth service impact 128 d, and send thesecond service impact report 126 b to a recipient at the user computingdevice 110 or other tools used in performing maintenance on the system.

In certain embodiments, the service impact report 126 b may then be usedto remedy the issue in a timely manner so that network, processing, andmemory resources can be used in a more efficient and timely manner. Forexample, processing and memory resources being reserved for thirdapplication 102 c may be redirected to perform other tasks during thetime of the delay in receiving workflow data 112 b from secondapplication 102 b. In addition, processor 116 may identify other sourcesof workflow data 112 b (e.g., applications 102 other than secondapplication 102 b) and route the workflow data 112 b from thatalternative source application 102 on a more timely basis so thatprocessing and memory resources used for third application 102 c are notwasted.

A service impact to one of the applications 102 a-102 i can be a delaysignificant enough to indicate the impacted application is notperforming at expected levels and either the impacted application isfunctioning poorly, supporting applications and networking services arefunctioning poorly, dependent applications are functioning poorly,applications the impacted applications are dependent on are functioningpoorly, or a combination thereof. As an example, if a communicationprotocol stack in the network 106 is saturated (data overflow) becauseof defective hardware or network congestion one or more of theapplications 102 a-102 i may experience delay that can impactperformance or result in loss of network resources. Poorly functioningapplications and services can lead to a default in service agreements, adelay in system throughput for an application service, and a completeshutdown of an application service.

Example Method for Network Integrated Diagnostic System and PredictiveAnalysis Tool

FIG. 2 illustrates an example flowchart of a method 200 for implementingthe network integrated diagnostic system and predictive analysis tool105. Modifications, additions, or omissions may be made to method 200.Method 200 may include more, fewer, or other operations. For example,operations may be performed in parallel or in any suitable order. Whileat times discussed as the application service architecture 100,plurality of networked connected applications 102, the networkintegrated diagnostic system and predictive analysis tool 105, network106, processor 116, or components of any of thereof performingoperations, any suitable system or components of the system may performone or more operations of the method 200. For example, one or moreoperations of method 200 may be implemented, at least in part, in theform of software instruction set 122 of FIG. 1 , stored onnon-transitory, tangible, machine-readable media (e.g., memory 118 ofFIG. 1 ) that when run by one or more processors (e.g., processor 116 ofFIG. 1 ) may cause the one or more processors to perform operations200-230.

Method 200 begins at operation 202 where memory 118 stores staticvariables 124 for the plurality of applications 102. Among other things,the static variables 124 comprise one or more processing time frames foreach application in the plurality of applications 102. The staticvariables 124 also comprise at least one time threshold value for eachapplication in the plurality of applications 102.

At operation 204, processor 116 receives real-time data 114 a for thefirst application 102 a that is generated during processing andcommunication (execution of the application) of the first application102 a. In some embodiments, the real-time data 114 a may be an RTT valuethat indicates a time delay value associated with executing the firstapplication 102 a. Alternatively, the real-time data 114 a may be a datainitiation and communication process completed message generated by thefirst application 102 a and the second application 102 b that can beused by the processor 116 to determine the time delay (RTT Value).

At operation 206, the processor 116 compares the time delay (delta T₁)associated with the real-time data 114 a with the processing time frameof the static variable 124 a, from the static variables 124, for thefirst application 102 a. At operation 208, the processor 116 determinesthe first service impact 128 a for the first application 102 a based onthe results of the comparison in operation 206. The first service impact128 a is realized if the time delay exceeds the processing time framefor the first application 102 a. In one embodiment, the processing timeframe can be a dynamic value adjustable in real-time based on dependentapplication functions, system load, or both.

At operation 210, the processor 116 predicts the probability of thesecond service impact 128 b for the second application 102 b by using analgorithmic model (artificial intelligence or machine learning basedalgorithmic model), a-priori knowledge base, or both and the firstservice impact 128 a for the first application 102 a and a timethreshold, as determined from the static variable 124 b associated withthe second application 102 b. The difference in the time delay (deltaT₁) and the processing time frame associated with the first serviceimpact 128 a of the first application 102 a, the time threshold of thesecond application 102 b, and the algorithmic model, a-priori knowledgebase, or any number and combination of these can be used in determiningthe probability. A probability value above a preset value can be used toindicate a service impact. At operation 212, processor 116 can generatea first service impact report 126 a based on the determined firstservice impact 128 a and the predicted second service impact 128 b andsend the report 126 a to the user computing device 110. In someembodiments, the probability value indicating an impact of service tothe second application 102 b can be a dynamic value adjustable inreal-time based on dependent application functions, system load, or anynumber and combination of these. At operation 214, the processor 116determines if there are more application to manage. If the processor 116determines there are no more application to manage, the execution endsat operation 230. If the processor 116 determines there are additionalapplications 102 to manage at operation 214, execution proceeds tooperation 216.

At operation 216, processor 116 receives real-time data 114 b for thesecond application 102 b that is generated during processing andcommunication (execution of the application) of the second application102 b. At operation 218, processor 116 compares the time delay (deltaT₂) associated with the real-time data 114 b with the processing timeframe of a static variable 124 b for the second application 102 b. Atoperation 220, processor 116 determines a third service impact 128 c forthe second application 102 b based on the results of the comparison. Athird service impact 128 c is realized if the time delay (delta T₂)exceeds the processing time frame for the second application 102 b. Atoperation 222, processor 116 predicts the probability of a fourthservice impact 128 d for the third application 102 c by using thealgorithmic model, a-priori knowledge base, or any number andcombination of these and the third service impact 128 c and a timethreshold associated with the third application 102 c. The difference inthe time delay (delta T₂) and the processing time frame associated withthe third service impact 128 c for the second application 102 b, thetime threshold associated with the third application 102 c, and thealgorithmic model, a-priori knowledge base, or any number andcombination of these can be used in determining the probability. Atoperation 224, the processor 116 generates a second service impactreport 126 b based on the determined third service impact 128 c and thepredicted fourth service impact 128 d and sends the second serviceimpact report 126 b to the user computing device 110. At operation 226,processor 116 determines if there are more applications 102 to manage.If the processor 116 determines there are no more applications tomanage, execution ends at operation 230. If the processor 116 determinesthere are more applications 102 to manage, the method 200 continues tooperation 228 where the processor 116 performs operations similar tooperations 218 through 226 for additional applications 102 to bemanaged. The processor 116 can continuously monitor workflows 104 a-104c and perform operations similar to those described above forapplications 102 of other workflows 104. Execution concludes atoperation 230. The description above in relation to operations 216-228are an abbreviated version of operations 202-212 but it should beunderstood that any functionality described in relation to operations202-212 are also applicable in relation to operations 216-228 and soforth.

While several embodiments have been provided in the present disclosure,it should be understood that the disclosed systems and methods might beembodied in many other specific forms without departing from the spiritor scope of the present disclosure. The present examples are to beconsidered as illustrative and not restrictive, and the intention is notto be limited to the details given herein. For example, the variouselements or components may be combined or integrated with another systemor certain features may be omitted, or not implemented.

In addition, techniques, systems, subsystems, and methods described andillustrated in the various embodiments as discrete or separate may becombined or integrated with other systems, modules, techniques, ormethods without departing from the scope of the present disclosure.Other items shown or discussed as coupled or directly coupled orcommunicating with each other may be indirectly coupled or communicatingthrough some interface, device, or intermediate component whetherelectrically, mechanically, or otherwise. Other examples of changes,substitutions, and alterations are ascertainable by one skilled in theart and could be made without departing from the spirit and scopedisclosed herein.

To aid the Patent Office, and any readers of any patent issued on thisapplication in interpreting the claims appended hereto, applicants notethat they do not intend any of the appended claims to invoke 35 U.S.C. §112(f) as it exists on the date of filing hereof unless the words “meansfor” or “step for” are explicitly used in the particular claim.

1. A system, comprising: a memory configured to store static variablesfor a plurality of applications, the static variables comprising aprocessing time frame required to satisfy a service level requirementfor a first application and a time threshold required to satisfy aservice level requirement for a second application; a processorcommunicatively coupled to the memory and configured to: receivereal-time data for the first application, wherein the first applicationis associated with the second application in a workflow, the real-timedata for the first application comprises a time delay associated withexecuting the first application; compare the real-time data for thefirst application with the static variables for the first application;determine a first service impact for the first application based on thecomparison of the real-time data for the first application with thestatic variables for the first application; predict a second serviceimpact for the second application based on the first service impact forthe first application and the time threshold associated with the secondapplication; generate a service impact report according to thedetermined first service impact and the predicted second service impact;and send the service impact report to at least one recipient computingdevice.
 2. The system of claim 1, wherein the processor is furtherconfigured to: receive real-time data for the second application,wherein the second application is associated with a third application inthe workflow; wherein the static variables further comprise a secondprocessing time frame required to satisfy a service level requirementfor the second application and a second time threshold required tosatisfy a service level requirement for the third application; comparethe real-time data of the second application with the static variablesof the second application; determine a third service impact for thesecond application based on the comparison of the real-time data for thesecond application with the static variables for the second application;predict a fourth service impact for the third application based on thethird service impact for the second application and the second timethreshold associated with the third application; generate a secondservice impact report according to the determined third service impactand the predicted fourth service impact; and send the second serviceimpact report to the at least one recipient computing device.
 3. Thesystem of claim 1, wherein the plurality of applications are networkconnected and configured to perform a time sensitive network serviceusing at least one hand-shake signal between the first application andat least one other application from the plurality of applications. 4.The system of claim 3, wherein the processor is further configured todetermine the time delay associated with executing the first applicationbased at least in part upon the at least one hand-shake signal.
 5. Thesystem of claim 4, wherein the processor is further configured tocompare the time delay associated with executing the first applicationagainst a threshold value associated with the first application todetermine the first service impact.
 6. The system of claim 1, whereinthe processor is further configured to predict a the service impactusing a knowledge base and an algorithmic model.
 7. The system of claim1, wherein the processor is further configured to determine an effect ofa the service impact based on the static variables.
 8. A method,comprising: storing static variables for a plurality of applications,the static variables comprising a processing time frame required tosatisfy a service level requirement for a first application and a timethreshold required to satisfy a service level requirement for a secondapplication; receiving real-time data for a first application, whereinthe first application is associated with the second application in aworkflow, the real-time data for the first application comprises a timedelay associated with executing the first application; comparing thereal-time data for the first application with the static variables forthe first application; determining a first service impact for the firstapplication based on the comparison of the real-time data for the firstapplication with the static variables for the first application;predicting a second service impact for the second application based onthe first service impact for the first application and the timethreshold associated with the second application; generating a serviceimpact report according to the determined first service impact and thepredicted second service impact; and sending the service impact reportto at least one recipient computing device.
 9. The method of claim 8,further comprising: receiving real-time data for the second application,wherein the second application is associated with a third application inthe workflow; wherein the static variables further comprise a secondprocessing time frame required to satisfy a service level requirementfor the second application and a second time threshold required tosatisfy a service level requirement for the third application; comparingthe real-time data of the second application with the static variablesof the second application; determining a third service impact for thesecond application based on the comparison of the real-time data for thesecond application with the static variables for the second application;predicting a fourth service impact for the third application based onthe third service impact for the second application and the second timethreshold associated with the third application; generating a secondservice impact report according to the determined third service impactand the predicted fourth service impact; and sending the second serviceimpact report to the at least one recipient computing device.
 10. Themethod of claim 8, wherein the plurality of applications are networkconnected and configured to perform a time sensitive network serviceusing at least one hand-shake signal between the first application andat least one other application from the plurality of applications. 11.The method of claim 10, further comprising determining the time delayassociated with executing the first application based at least in partupon the at least one hand-shake signal.
 12. The method of claim 11,further comprising comparing the time delay associated with executingthe first application against a threshold value associated with thefirst application to determine the first service impact.
 13. The methodof claim 8, further comprising predicting a the service impact using aknowledge base and an algorithmic model.
 14. The method of claim 8,further comprising determining an effect of a the service impact basedon the static variables.
 15. A non-transitory computer-readable storagemedium, storing instructions that when executed by a processor, causethe processor to: store static variables for a plurality ofapplications, the static variables comprising a processing time framerequired to satisfy a service level requirement for a first applicationand a time threshold required to satisfy a service level requirement fora second application; receive real-time data for a first application,wherein the first application is associated with the second applicationin a workflow, the real-time data for the first application comprises atime delay associated with executing the first application; compare thereal-time data for the first application with the static variables forthe first application; determine a first service impact for the firstapplication based on the comparison of the real-time data for the firstapplication with the static variables for the first application; predicta second service impact for the second application based on the firstservice impact for the first application and the time thresholdassociated with the second application; generate a service impact reportaccording to the determined first service impact and the predictedsecond service impact; and send the service impact report to at leastone recipient computing device.
 16. The non-transitory computer-readablestorage medium of claim 15, wherein the instructions further cause theprocessor to : receive real-time data for the second application,wherein the second application is associated with a third application inthe workflow; wherein the static variables further comprise a secondprocessing time frame required to satisfy a service level requirementfor the second application and a second time threshold required tosatisfy a service level requirement for the third application; comparethe real-time data of the second application with the static variablesof the second application; determine a third service impact for thesecond application based on the comparison of the real-time data for thesecond application with the static variables for the second application;predict a fourth service impact for the third application based on thethird service impact for the second application and the second timethreshold associated with the third application; generate a secondservice impact report according to the determined third service impactand the predicted fourth service impact; and send the second serviceimpact report to the at least one recipient computing device.
 17. Thenon-transitory computer-readable storage medium of claim 15, wherein theplurality of applications are network connected and configured toperform a time sensitive network service using at least one hand-shakesignal between the first application and at least one other applicationfrom the plurality of applications.
 18. The non-transitorycomputer-readable storage medium of claim 17, wherein the instructionsfurther cause the processor to determine the time delay associated withexecuting the first application based at least in part upon the at leastone hand-shake signal.
 19. The non-transitory computer-readable storagemedium of claim 17, wherein the instructions further cause the processorto compare the time delay associated with executing the firstapplication against a threshold value associated with the firstapplication to determine the first service impact.
 20. Thenon-transitory computer-readable storage medium of claim 15, wherein theinstructions further cause the processor to predict a the service impactusing a knowledge base and an algorithmic model.